






"""simple docstring"""


from typing import List, Optional, Union

from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType




class 						__UpperCamelCase     (     UpperCamelCase						):
					lowercase_    :				Optional[int]      =  ["""image_processor""", """tokenizer"""]
					lowercase_    :				int      =  """BlipImageProcessor"""
					lowercase_    :				Union[str, Any]      =  ("""BertTokenizer""", """BertTokenizerFast""")
					def __init__(							self							:	List[Any]     , UpperCAmelCase							:	List[Any]     , UpperCAmelCase							:	int   )	->       Optional[Any]:
											lowerCAmelCase   :List[str]						=			False
											super().__init__(UpperCAmelCase     , UpperCAmelCase   )
											lowerCAmelCase   :Optional[int]						=			self.image_processor
					def __call__(							self							:	int     , UpperCAmelCase							:	ImageInput = None     , UpperCAmelCase							:	Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None     , UpperCAmelCase							:	bool = True     , UpperCAmelCase							:	Union[bool, str, PaddingStrategy] = False     , UpperCAmelCase							:	Union[bool, str, TruncationStrategy] = None     , UpperCAmelCase							:	Optional[int] = None     , UpperCAmelCase							:	int = 0     , UpperCAmelCase							:	Optional[int] = None     , UpperCAmelCase							:	Optional[bool] = None     , UpperCAmelCase							:	bool = False     , UpperCAmelCase							:	bool = False     , UpperCAmelCase							:	bool = False     , UpperCAmelCase							:	bool = False     , UpperCAmelCase							:	bool = False     , UpperCAmelCase							:	bool = True     , UpperCAmelCase							:	Optional[Union[str, TensorType]] = None     , **UpperCAmelCase							:	Optional[int]     , )	->       BatchEncoding:
											if images is None and text is None:
																	raise ValueError('You have to specify either images or text.'   )

											# Get only text
											if images is None:
																	lowerCAmelCase   :List[str]						=			self.tokenizer
																	lowerCAmelCase   :Optional[Any]						=			self.tokenizer(
																	    text=UpperCAmelCase     , add_special_tokens=UpperCAmelCase     , padding=UpperCAmelCase     , truncation=UpperCAmelCase     , max_length=UpperCAmelCase     , stride=UpperCAmelCase     , pad_to_multiple_of=UpperCAmelCase     , return_attention_mask=UpperCAmelCase     , return_overflowing_tokens=UpperCAmelCase     , return_special_tokens_mask=UpperCAmelCase     , return_offsets_mapping=UpperCAmelCase     , return_token_type_ids=UpperCAmelCase     , return_length=UpperCAmelCase     , verbose=UpperCAmelCase     , return_tensors=UpperCAmelCase     , **UpperCAmelCase     , )
																	return text_encoding

											# add pixel_values
											lowerCAmelCase   :Dict						=			self.image_processor(UpperCAmelCase     , return_tensors=UpperCAmelCase   )

											if text is not None:
																	lowerCAmelCase   :Union[str, Any]						=			self.tokenizer(
																	    text=UpperCAmelCase     , add_special_tokens=UpperCAmelCase     , padding=UpperCAmelCase     , truncation=UpperCAmelCase     , max_length=UpperCAmelCase     , stride=UpperCAmelCase     , pad_to_multiple_of=UpperCAmelCase     , return_attention_mask=UpperCAmelCase     , return_overflowing_tokens=UpperCAmelCase     , return_special_tokens_mask=UpperCAmelCase     , return_offsets_mapping=UpperCAmelCase     , return_token_type_ids=UpperCAmelCase     , return_length=UpperCAmelCase     , verbose=UpperCAmelCase     , return_tensors=UpperCAmelCase     , **UpperCAmelCase     , )
											else:
																	lowerCAmelCase   :Union[str, Any]						=			None

											if text_encoding is not None:
																	encoding_image_processor.update(UpperCAmelCase   )

											return encoding_image_processor
					def        UpperCAmelCase__ (							self							:	List[str]     , *UpperCAmelCase							:	Union[str, Any]     , **UpperCAmelCase							:	Union[str, Any]   )	->       Any:
											return self.tokenizer.batch_decode(*UpperCAmelCase     , **UpperCAmelCase   )
					def        UpperCAmelCase__ (							self							:	str     , *UpperCAmelCase							:	Union[str, Any]     , **UpperCAmelCase							:	Union[str, Any]   )	->       List[str]:
											return self.tokenizer.decode(*UpperCAmelCase     , **UpperCAmelCase   )
					@property
					def        UpperCAmelCase__ (							self							:	Optional[int]   )	->       Optional[int]:
											lowerCAmelCase   :Tuple						=			self.tokenizer.model_input_names
											lowerCAmelCase   :int						=			self.image_processor.model_input_names
											return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names   )   )







"""simple docstring"""


import itertools
import math


def        UpperCAmelCase      (					a__ ):





						'''simple docstring'''






						if 1 < number < 4:
												# 2 and 3 are primes
												return True
						elif number < 2 or number % 2 == 0 or number % 3 == 0:
												# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
												return False

						# All primes number are in format of 6k +/- 1
						for i in range(5     ,      int(math.sqrt(a__ ) + 1 )     ,      6 ):
												if number % i == 0 or number % (i + 2) == 0:
																		return False
						return True


def        UpperCAmelCase      (					):





						'''simple docstring'''






						lowerCAmelCase   :str						=			2
						while True:
												if is_prime(a__ ):
																		yield num
												num += 1


def        UpperCAmelCase      (					a__ = 1_00_01 ):





						'''simple docstring'''






						return next(itertools.islice(prime_generator()     ,      nth - 1     ,      a__ ) )


if __name__ == "__main__":
		print(F"""{solution() = }""")